Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Direction-Based Segmentation of Retinal Blood Vessels Read chapter Read first chapter

2017 | OriginalPaper | Chapter

An Approximate Support Vector Machines Solver with Budget Control

Authors : Carles R. Riera, Oriol Pujol

Published in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We propose a novel approach to approximately solve online kernel Support Vector Machines (SVM) with the number of support vectors set beforehand. To this aim, we modify the original formulation introducing a new constraint that penalizes the deviation with respect to the desired number of support vectors. The resulting problem is solved using stochastic subgradient methods with block coordinate descent. Comparison with state-of-the-art online methods shows very promising results.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Improving Nearest Neighbor Based Multi-target Prediction Through Metric Learning
next chapter Multi-biometric Template Protection on Smartphones: An Approach Based on Binarized Statistical Features and Bloom Filters
Footnotes
1
f(x) is called to be quasi-concave when the set \(\{x | f(x) = \epsilon \}\) is a convex set.
 
Literature
1.
Bordes, A., Ertekin, E., Weston, J., Bottou, L.: Fast Kernel classifiers with online and active learning. J. Mach. Learn. Res. 6, 1579–1619 (2005)MathSciNetMATH
2.
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT’2010, pp. 177–186. Springer, Heidelberg (2010)
3.
4.
Joachims, T., Yu, C.N.J.: Sparse Kernel SVMs via cutting-plane training. Mach. Learn. 76(2–3), 179–193 (2009)CrossRef
5.
Jung, H.G., Kim, G.: Support vector number reduction: survey and experimental evaluations. IEEE Trans. Intel. Transp. Syst. 15(2), 463–476 (2014)CrossRef
6.
Keerthi, S.S., Decoste, D.: Building support vector machines with reduced classifier complexity. J. Mach. Learn. Res. 7, 1493–1515 (2006)MathSciNetMATH
7.
Platt, J.C.: Sequential minimal optimization: a fast algorithm for training support vector machines. Adv. Kernel MethodsSupport Vector Learn. 208, 1–21 (1998)
8.
Shalev-Shwartz, S., Singer, Y., Srebro, N., Cotter, A.: Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. 127(1), 3–30 (2011)MathSciNetCrossRefMATH
9.
Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244 (2001)MathSciNetMATH
10.
Zhu, J., Rosset, S., Hastie, T., Tibshirani, R.: 1-norm support vector machines. Adv. Neural Inf. Process. Syst. 16(1), 49–56 (2004)
Metadata
Title
An Approximate Support Vector Machines Solver with Budget Control
Authors
Carles R. Riera
Oriol Pujol
Copyright Year
2017
Book
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Print ISBN: 978-3-319-52276-0

Electronic ISBN: 978-3-319-52277-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-52277-7_46

Premium Partner

    Image Credits